Question 1

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Options :

A : Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

B : In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

C : In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

D : Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Answer: B

Question 2

A data pipeline uses Cloud Pub/Sub for ingesting data. The data is stored in topics and a Dataflow workflow reads from a subscription to that topic, processes the data, and writes output to BigQuery. What is the recommended way to authenticate when reading data from Cloud Pub/Sub?

Options :

A : Custom role

B : Google Workspace Identity

C : Use service accounts

D : Basic role

Answer: C

Question 3

Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

Options :

A : Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

B : Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

C : Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

D : Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: D

Question 4

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query: SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:What is the most likely cause of the delay for this query?

Options :

A : Users are running too many concurrent queries in the system

B : The [myproject:mydataset.mytable] table has too many partitions

C : Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values

D : Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Answer: A

Question 5

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

Options :

A : Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

B : Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.

C : Change the data pipeline to use BigQuery for storing stock trades, and update your application.

D : Use Cloud Dataflow to write summary of each day’s stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Answer: A

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